class_docstr: Base class for a deep forecaster model
code: def _train(
      self, train_data: pd.DataFrame, train_config=None
  ) -> pd.DataFrame:
      config = self.config
  
      # creating model before the training
      self._create_model()
  
      total_dataset = RollingWindowDataset(
          train_data,
          n_past=config.n_past,
          n_future=config.max_forecast_steps,
          batch_size=config.batch_size,
          target_seq_index=None,  # have to set None, we use target_seq_index later in the training, if not this is a bug
          ts_encoding=config.ts_encoding,
          valid_fraction=config.valid_fraction,
          flatten=False,
          shuffle=True,
          validation=False,
      )
  
      train_steps = len(total_dataset)
      logger.info(f"Training steps each epoch: {train_steps}")
  
      bar = ProgressBar(total=config.num_epochs)
      early_stopping = (
          EarlyStopping(patience=config.early_stop_patience)
          if config.early_stop_patience
          else None
      )
  
      # start training
      for epoch in range(config.num_epochs):
          train_loss = []
          self.deep_model.train()
          total_dataset.seed = epoch + 1
  
          for i, batch in enumerate(total_dataset):
              self.optimizer.zero_grad()
  
              loss, _, _ = self._get_batch_model_loss_and_outputs(
                  self._convert_batch_to_tensors(batch)
              )
              train_loss.append(loss.item())
  
              loss.backward()
              if config.clip_gradient is not None:
                  torch.nn.utils.clip_grad_norm(
                      self.model.parameters(), config.clip_gradient
                  )
  
              self.optimizer.step()
  
          train_loss = np.average(train_loss)
  
          # set validation flag
          total_dataset.validation = True
          _, val_loss = self._get_np_loss_and_prediction(total_dataset)
          total_dataset.validation = False
  
          if bar is not None:
              bar.print(
                  epoch + 1,
                  prefix="",
                  suffix=f"Train Loss: {train_loss: .4f}, Validation Loss: {val_loss: .4f}",
              )
  
          if early_stopping is not None:
              early_stopping(val_loss, self.deep_model)
              if early_stopping.early_stop:
                  logger.info(
                      f"Early stopping with {config.early_stop_patience} patience"
                  )
                  break
  
      if early_stopping is not None:
          early_stopping.load_best_model(self.deep_model)
          logger.info(
              f"Load the best model with validation loss: {early_stopping.val_loss_min: .4f}"
          )
  
      logger.info("End of the training loop")
  
      # get predictions
      total_dataset.shuffle = False
      total_dataset.validation = None
      pred, _ = self._get_np_loss_and_prediction(total_dataset)
  
      # since the model predicts multiple steps, we concatenate all the first steps together
      columns = (
          train_data.columns
          if self.target_seq_index is None
          else [self.target_name]
      )
      column_index = train_data.index[
          config.n_past : (
              len(train_data) - config.max_forecast_steps + 1
          )
      ]
      return (
          pd.DataFrame(pred[:, 0], index=column_index, columns=columns),
          None,
      )
  
imports: import copy
  import logging
  import numpy
  import pandas
  from scipy.stats import norm
  from typing import List, Optional, Tuple, Union
  from merlion.models.deep_base import DeepConfig, DeepModelBase
  from merlion.models.forecast.base import ForecasterBase, ForecasterConfig
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
  from merlion.models.utils.time_features import get_time_features
  from merlion.models.utils.early_stopping import EarlyStopping
  from merlion.transform.base import TransformBase, Identity
  from merlion.transform.factory import TransformFactory
  from merlion.utils.misc import initializer, ProgressBar
  from merlion.utils.time_series import to_pd_datetime, to_timestamp, TimeSeries, AggregationPolicy, MissingValuePolicy
label_desc: Use image node_id 5 for calling the DeepForecaster obj's underlying member method code with example usage: obj._train(train_data, train_config) and returns: pd,  None